Summary

Diabetes mellitus is considered an epidemic of the 21st century, increasing dramatically in recent years, with a 9% global prevalence reported in 2014. The International Diabetes Federation estimates that 425 million people had diabetes in 2017, increasing to 629 million in 2045. The burden of increase is highest in LMICs compared with high-income countries (HICs). Diabetic retinopathy (DR) is a common microvascular complication of diabetes mellitus (types I and 2), which can lead to visual impairment and blindness if not detected early and treated. People with vision‐threatening DR have been shown to have increased risk of mental health issues, depression and loss of productivity.

DR is the leading cause of visual impairment and blindness in the working age population. DR is recognised by the World Health Organisation as a priority public health concern in LMICs. In HICs, DR Screening is conducted through systematic national-level programs, but LMICs are unlikely to have full population-based screening programmes owing to limited resources including technology and trained personnel. Screening programmes in HICs typically use retinal photography in community settings that are then graded by eyecare personnel. Potential cases of DR are then flagged for further clinical assessment or management.

By contrast, LMICs rely on opportunistic screening and case detection.  A limited healthcare workforce is a major problem in most LMICs, with very few ophthalmologists to conduct ocular examinations. The reasons for the unavailability of DR Screening in LMIC settings are mostly attributed to the lack of skilled human resources, financial resources, geographical challenges, and evidence of what works in the local system. This project proposes to develop a cost effective computer-aided tool to detect DR at an early stage, prior to the occurrence of irreversible vision loss, using an appropriate set of features retrieved from retinal images (captured by a hand-held camera) along with Artificial Intelligence Deep Learning techniques.

Previous work has demonstrated that this system can be used by a non-specialist medical worker (with minimal training) in a range of environments (e.g., community clinic or patient’s home). Hand-held cameras are easy to transport, require little electrical power, and are user-friendly.

Three project stages will deliver the overarching project aim:

-Development of algorithms and AI system to effectively analyse retinal images with DR -Trial of system with Prof. Peto in UK grading centre to compare with conventional retinal photography and DR grading -Trial of system in the LMIC areas of Sri Lanka and India with established research partners This PhD can only be realised with the interdisciplinary connection of the ISRC, who bring expertise in current deep learning topics and computer vision algorithms, and existing partnership with the LMICs; while the Centre for Optometry and Vision Science academics bring expertise in retinal imaging, knowledge of extraction of key features from the retina, clinical management of DR, and partnership with Prof Tunde Peto. She is a world-recognised expert in the epidemiology of DR and is Head of the DR Screening programme in NI.


Essential criteria

  • To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
  • Research proposal of 1500 words detailing aims, objectives, milestones and methodology of the project
  • A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Masters at 70%
  • For VCRS Awards, Masters at 75%
  • Publications - peer-reviewed
  • Experience of presentation of research findings
  • Applicants will be shortlisted if they have an average of 75% or greater in a first (honours) degree (or a GPA of 8.75/10). For applicants with a first degree average in the range of 70% to 74% (GPA 3.3): If they are undertaking an Masters, then the average of their first degree marks and their Masters marks will be used for shortlisting.

    The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:

    Department for the Economy (DFE)

    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £15,285 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

    Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies



The Doctoral College at Ulster University


Reviews

Profile picture of Adrian Johnston

As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day

Adrian Johnston - PhD in Informatics

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Profile picture of Xin Wei

I received the bachelor’s of engineering degree in computer science and technology from Shangrao Normal University, Jiangxi, China, in 2013; and the master’s degree in computer application and technology from the School of Mathematics and Computer Science, Fujian Normal University, China. When I was pursuing a PhD degree at Ulster University, I continued my research on face recognition and image representation.This long journey has only been possible due to the constant support and encouragement of my first supervisor. I also like to thank my second supervisor for his patience, support and guidance during my research studies. My favourite memory was the days of exercising, gathering and playing with my friends here. If I could speak to myself at the start of my PhD, the best piece of advice I would give myself would be "submit more papers to Journals instead of conferences".

Xin Wei - PhD in Computer Science and Informatics


Profile picture of Jyotsna Talreja Wassan

In the whole PhD ordeal, my supervisory team played a tremendous role:- they are three in a million. They are perfect supervisors who perfectly know which milestones or pathways to be taken during research initiatives, and they understand the roles of virtually all stages in the journey of PhD. They showcased superior abilities in managing and motivating me evoking high standards; demonstrating a commitment to excellence. Jane and Haiying guided me as their daughter and Fiona turned out to be the best of friends.I heard from “Eleanor Roosevelt” that “The future belongs to those who believe in the beauty of their dreams.” The dream with which I grew up to become a Doctor one day, has finally come true. In the journey of PhD, I embraced that a PhD is not just the highest degree in Education but rather it is a life experience where perseverance is the key. I can never forget words from my external examiner Prof Yike Guo, from Imperial College London. His words

Jyotsna Talreja Wassan - PhD in Computer Science and Informatics